OpenRaiser/Intern-Atlas huggingface dataset:
Category Trees
Venue: arXiv cs.LG 2020
Year: 2020

This paper presents a batch classifier that has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. Each category is represented by a classifier, where each classifier classifies its own subset of data rows, using batch input values to represent the centroid. The first change is to use the category centroid as the desired category output. When the classifier represents more than one category, it creates a new layer and splits, to represent each category separately in the new layer. The second change therefore, is to allow the classifier to branch to new levels when there is a split in the data, or when some data rows are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests further innovations, by adding fixed value ranges through bands, for each column or feature of the input dataset. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest can be classified using the classifier technique. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art. The paper also discusses a biological analogy with neurons and neuron links.
Position Information-based NOMA for Downlink and Uplink Transmission in Mobile Scenarios
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, a non-orthogonal multiple access (NOMA) system with partial channel state information (CSI) for downlink and uplink transmission in mobile scenarios is considered, i.e., users are deployed randomly and will move casually around the base station (BS). In this case, the channel gain of each user varies over time, which has an influence on the performance of conventional NOMA. An analytical framework is developed to evaluate the impact of position estimation deviation in terms of decoding order error probability, average sum rate and outage probability. Based on the framework, dynamic power allocation (DPA) for downlink NOMA and dynamic power control (DPC) for uplink NOMA are put forward to optimize the outage performance with user distance information. It has been shown that the performance of NOMA relies on accurate user position information. To this end, two algorithms based on position filtering are proposed to improve the accuracy of user position. Monte Carlo simulation is presented to demonstrate the improvement of spectrum efficiency and outage performance. Simulation results verify the accuracy of the proposed analytical framework.
Alpha-Integration Pooling for Convolutional Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Convolutional neural networks (CNNs) have achieved remarkable performance in many applications, especially in image recognition tasks. As a crucial component of CNNs, sub-sampling plays an important role for efficient training or invariance property, and max-pooling and arithmetic average-pooling are commonly used sub-sampling methods. In addition to the two pooling methods, however, there could be many other pooling types, such as geometric average, harmonic average, and so on. Since it is not easy for algorithms to find the best pooling method, usually the pooling types are assumed a priority, which might not be optimal for different tasks. In line with the deep learning philosophy, the type of pooling can be driven by data for a given task. In this paper, we propose {\it $α$-integration pooling} ($α$I-pooling), which has a trainable parameter $α$ to find the type of pooling. $α$I-pooling is a general pooling method including max-pooling and arithmetic average-pooling as a special case, depending on the parameter $α$. Experiments show that $α$I-pooling outperforms other pooling methods including max-pooling, in image recognition tasks. Also, it turns out that each layer has different optimal pooling type.
TimeCrypt: Encrypted Data Stream Processing at Scale with Cryptographic Access Control
Venue: arXiv cs.CR 2020
Year: 2020

A growing number of devices and services collect detailed time series data that is stored in the cloud. Protecting the confidentiality of this vast and continuously generated data is an acute need for many applications in this space. At the same time, we must preserve the utility of this data by enabling authorized services to securely and selectively access and run analytics. This paper presents TimeCrypt, a system that provides scalable and real-time analytics over large volumes of encrypted time series data. TimeCrypt allows users to define expressive data access and privacy policies and enforces it cryptographically via encryption. In TimeCrypt, data is encrypted end-to-end, and authorized parties can only decrypt and verify queries within their authorized access scope. Our evaluation of TimeCrypt shows that its memory overhead and performance are competitive and close to operating on data in the clear.
Withdrawing the BGP Re-Routing Curtain: Understanding the Security Impact of BGP Poisoning via Real-World Measurements
Venue: arXiv cs.CR 2020
Year: 2020

The security of the Internet's routing infrastructure has underpinned much of the past two decades of distributed systems security research. However, the converse is increasingly true. Routing and path decisions are now important for the security properties of systems built on top of the Internet. In particular, BGP poisoning leverages the de facto routing protocol between Autonomous Systems (ASes) to maneuver the return paths of upstream networks onto previously unusable, new paths. These new paths can be used to avoid congestion, censors, geo-political boundaries, or any feature of the topology which can be expressed at an AS-level. Given the increase in BGP poisoning usage as a security primitive, we set out to evaluate poisoning feasibility in practice beyond simulation. To that end, using an Internet-scale measurement infrastructure, we capture and analyze over 1,400 instances of BGP poisoning across thousands of ASes as a mechanism to maneuver return paths of traffic. We analyze in detail the performance of steering paths, the graph-theoretic aspects of available paths, and re-evaluate simulated systems with this data. We find that the real-world evidence does not completely support the findings from simulated systems published in the literature. We also analyze filtering of BGP poisoning across types of ASes and ISP working groups. We explore the connectivity concerns when poisoning by reproducing a decade old experiment to uncover the current state of an Internet triple the size. We build predictive models for understanding an ASes' vulnerability to poisoning. Finally, an exhaustive measurement of an upper bound on the maximum path length of the Internet is presented, detailing how security research should react to ASes leveraging poisoned long paths. In total, our results and analysis expose the real-world impact of BGP poisoning on past and future security research.
Construction of isodual codes from polycirculant matrices
Venue: arXiv cs.CR 2020
Year: 2020

Double polycirculant codes are introduced here as a generalization of double circulant codes. When the matrix of the polyshift is a companion matrix of a trinomial, we show that such a code is isodual, hence formally self-dual. Numerical examples show that the codes constructed have optimal or quasi-optimal parameters amongst formally self-dual codes. Self-duality, the trivial case of isoduality, can only occur over $ \F_2$ in the double circulant case. Building on an explicit infinite sequence of irreducible trinomials over $\F_2,$ we show that binary double polycirculant codes are asymptotically good.
The GaussianSketch for Almost Relative Error Kernel Distance
Venue: arXiv cs.LG 2020
Year: 2020

We introduce two versions of a new sketch for approximately embedding the Gaussian kernel into Euclidean inner product space. These work by truncating infinite expansions of the Gaussian kernel, and carefully invoking the RecursiveTensorSketch [Ahle et al. SODA 2020]. After providing concentration and approximation properties of these sketches, we use them to approximate the kernel distance between points sets. These sketches yield almost $(1+\varepsilon)$-relative error, but with a small additive $α$ term. In the first variants the dependence on $1/α$ is poly-logarithmic, but has higher degree of polynomial dependence on the original dimension $d$. In the second variant, the dependence on $1/α$ is still poly-logarithmic, but the dependence on $d$ is linear.
Spectral Efficiency Analysis in Presence of Correlated Gamma-Lognormal Desired and Interfering Signals
Venue: arXiv cs.IT 2020
Year: 2020

Spectral efficiency analysis in presence of correlated interfering signals is very important in modern generation wireless networks where there is aggressive frequency reuse with a dense deployment of access points. However, most works available in literature either address the effect of correlated interfering signals or include interferer activity, but not both. Further, available literature has also addressed the effect of large-scale fading (shadowing and distance-dependent path loss) only, however, has fallen short of including the composite effect of the line of sight and non-line of sight small-scale fading. The correlation of desired signals with interfering signals due to shadowing has also not been considered in existing literature. In this work, we present a comprehensive analytical signal to interference power ratio evaluation framework addressing all the above mentioned important components to the model in a holistic manner. In this analysis, we extend and apply the Moment Generating Function-matching method to such systems so that correlation and activity of lognormal random variables can be included with high accuracy. We compare the analytical results against realistic channel model based extensive Monte-Carlo simulation for mmWave and sub-6 GHz in both indoor and outdoor scenarios. the performance of the model is depicted in terms of mean, alpha-percentile outage spectral efficiency and Kullback-Leibler divergence and Kolmogorov-Smirnov distance.
Spherical clustering of users navigating 360° content
Venue: arXiv cs.MM 2020
Year: 2020

In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on their common navigation patterns is a first direction to understand users behaviour. However, classical clustering techniques fail in identifying these common paths, since they are usually focused on minimizing a simple distance metric. In this paper, we argue that minimizing the distance metric does not necessarily guarantee to identify users that experience similar navigation path in the VR domain. Therefore, we propose a graph-based method to identify clusters of users who are attending the same portion of the spherical content over time. The proposed solution takes into account the spherical geometry of the content and aims at clustering users based on the actual overlap of displayed content among users. Our method is tested on real VR user navigation patterns. Results show that our solution leads to clusters in which at least 85% of the content displayed by one user is shared among the other users belonging to the same cluster.
SLIM: Simultaneous Logic-in-Memory Computing Exploiting Bilayer Analog OxRAM Devices
Venue: arXiv cs.ET 2020
Year: 2020

Von Neumann architecture based computers isolate/physically separate computation and storage units i.e. data is shuttled between computation unit (processor) and memory unit to realize logic/ arithmetic and storage functions. This to-and-fro movement of data leads to a fundamental limitation of modern computers, known as the memory wall. Logic in-Memory (LIM) approaches aim to address this bottleneck by computing inside the memory units and thereby eliminating the energy-intensive and time-consuming data movement. However, most LIM approaches reported in literature are not truly "simultaneous" as during LIM operation the bitcell can be used only as a Memory cell or only as a Logic cell. The bitcell is not capable of storing both the Memory/Logic outputs simultaneously. Here, we propose a novel 'Simultaneous Logic in-Memory' (SLIM) methodology that allows to implement both Memory and Logic operations simultaneously on the same bitcell in a non-destructive manner without losing the previously stored Memory state. Through extensive experiments we demonstrate the SLIM methodology using non-filamentary bilayer analog OxRAM devices with NMOS transistors (2T-1R bitcell). Detailed programming scheme, array level implementation and controller architecture are also proposed. Furthermore, to study the impact of introducing SLIM array in the memory hierarchy, a simple image processing application (edge detection) is also investigated. It has been estimated that by performing all computations inside the SLIM array, the total Energy Delay Product (EDP) reduces by ~ 40x in comparison to a modern-day computer. EDP saving owing to reduction in data transfer between CPU Memory is observed to be ~ 780x.
A Simulated Cyberattack on Twitter: Assessing Partisan Vulnerability to Spear Phishing and Disinformation ahead of the 2018 U.S. Midterm Elections
Venue: arXiv cs.SI 2020
Year: 2020

State-sponsored "bad actors" increasingly weaponize social media platforms to launch cyberattacks and disinformation campaigns during elections. Social media companies, due to their rapid growth and scale, struggle to prevent the weaponization of their platforms. This study conducts an automated spear phishing and disinformation campaign on Twitter ahead of the 2018 United States Midterm Elections. A fake news bot account - the @DCNewsReport - was created and programmed to automatically send customized tweets with a "breaking news" link to 138 Twitter users, before being restricted by Twitter. Overall, one in five users clicked the link, which could have potentially led to the downloading of ransomware or the theft of private information. However, the link in this experiment was non-malicious and redirected users to a Google Forms survey. In predicting users' likelihood to click the link on Twitter, no statistically significant differences were observed between right-wing and left-wing partisans, or between Web users and mobile users. The findings signal that politically expressive Americans on Twitter, regardless of their party preferences or the devices they use to access the platform, are at risk of being spear phishing on social media.
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon
Venue: arXiv cs.LG 2020
Year: 2020

This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive to compute or mathematically not well defined. Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized way. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.
Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road
Venue: arXiv cs.CV 2020
Year: 2020

This paper introduces an approach to produce accurate 3D detection boxes for objects on the ground using single monocular images. We do so by merging 2D visual cues, 3D object dimensions, and ground plane constraints to produce boxes that are robust against small errors and incorrect predictions. First, we train a single-shot convolutional neural network (CNN) that produces multiple visual and geometric cues of interest: 2D bounding boxes, 2D keypoints of interest, coarse object orientations and object dimensions. Subsets of these cues are then used to poll probable ground planes from a pre-computed database of ground planes, to identify the "best fit" plane with highest consensus. Once identified, the "best fit" plane provides enough constraints to successfully construct the desired 3D detection box, without directly predicting the 6DoF pose of the object. The entire ground plane polling (GPP) procedure is constructed as a non-parametrized layer of the CNN that outputs the desired "best fit" plane and the corresponding 3D keypoints, which together define the final 3D bounding box. Doing so allows us to poll thousands of different ground plane configurations without adding considerable overhead, while also creating a single CNN that directly produces the desired output without the need for post processing. We evaluate our method on the 2D detection and orientation estimation benchmark from the challenging KITTI dataset, and provide additional comparisons for 3D metrics of importance. This single-stage, single-pass CNN results in superior localization and orientation estimation compared to more complex and computationally expensive monocular approaches.
Fast quasi-conformal regional flattening of the left atrium
Venue: arXiv cs.GR 2020
Year: 2020

Two-dimensional representation of 3D anatomical structures is a simple and intuitive way for analysing patient information across populations and image modalities. It also allows convenient visualizations that can be included in clinical reports for a fast overview of the whole structure. While cardiac ventricles, especially the left ventricle, have an established standard representation (e.g. bull's eye plot), the 2D depiction of the left atrium (LA) is challenging due to its sub-structural complexity including the pulmonary veins (PV) and the left atrial appendage (LAA). Quasi-conformal flattening techniques, successfully applied to cardiac ventricles, require additional constraints in the case of the LA to place the PV and LAA in the same geometrical 2D location for different cases. Some registration-based methods have been proposed but 3D (or 2D) surface registration is time-consuming and prone to errors if the geometries are very different. We propose a novel atrial flattening methodology where a quasi-conformal 2D map of the LA is obtained quickly and without errors related to registration. In our approach, the LA is divided into 5 regions which are then mapped to their analogue two-dimensional regions. A dataset of 67 human left atria from magnetic resonance images (MRI) was studied to derive a population-based 2D LA template representing the averaged relative locations of the PVs and LAA. The clinical application of the proposed methodology is illustrated on different use cases including the integration of MRI and electroanatomical data.
RelationNet2: Deep Comparison Columns for Few-Shot Learning
Venue: arXiv cs.CV 2020
Year: 2020

Few-shot deep learning is a topical challenge area for scaling visual recognition to open ended growth of unseen new classes with limited labeled examples. A promising approach is based on metric learning, which trains a deep embedding to support image similarity matching. Our insight is that effective general purpose matching requires non-linear comparison of features at multiple abstraction levels. We thus propose a new deep comparison network comprised of embedding and relation modules that learn multiple non-linear distance metrics based on different levels of features simultaneously. Furthermore, to reduce over-fitting and enable the use of deeper embeddings, we represent images as distributions rather than vectors via learning parameterized Gaussian noise regularization. The resulting network achieves excellent performance on both miniImageNet and tieredImageNet.
ShapeSearch: A Flexible and Efficient System for Shape-based Exploration of Trendlines
Venue: arXiv cs.DB 2020
Year: 2020

Identifying trendline visualizations with desired patterns is a common and fundamental data exploration task. Existing visual analytics tools offer limited flexibility and expressiveness for such tasks, especially when the pattern of interest is under-specified and approximate, and do not scale well when the pattern searching needs are ad-hoc, as is often the case. We propose ShapeSearch, an efficient and flexible pattern-searching tool, that enables the search for desired patterns via multiple mechanisms: sketch, natural-language, and visual regular expressions. We develop a novel shape querying algebra, with a minimal set of primitives and operators that can express a large number of ShapeSearch queries, and design a natural-language and regex-based parser to automatically parse and translate user queries to the algebra representation. To execute these queries within interactive response times, ShapeSearch uses a fast shape algebra-based execution engine with query-aware optimizations, and perceptually-aware scoring methodologies. We present a thorough evaluation of the system, including a general-purpose user study, a case study involving genomic data analysis, as well as performance experiments, comparing against state-of-the-art time series shape matching approaches---that together demonstrate the usability and scalability of ShapeSearch.
Applying the swept rule for solving explicit partial differential equations on heterogeneous computing systems
Venue: arXiv cs.MS 2020
Year: 2020

Applications that exploit the architectural details of high-performance computing (HPC) systems have become increasingly invaluable in academia and industry over the past two decades. The most important hardware development of the last decade in HPC has been the General Purpose Graphics Processing Unit (GPGPU), a class of massively parallel devices that now contributes the majority of computational power in the top 500 supercomputers. As these systems grow, small costs such as latency---due to the fixed cost of memory accesses and communication---accumulate in a large simulation and become a significant barrier to performance. The swept time-space decomposition rule is a communication-avoiding technique for time-stepping stencil update formulas that attempts to reduce latency costs. This work extends the swept rule by targeting heterogeneous, CPU/GPU architectures representing current and future HPC systems. We compare our approach to a naive decomposition scheme with two test equations using an MPI+CUDA pattern on 40 processes over two nodes containing one GPU. The swept rule produces a factor of 1.9 to 23 speedup for the heat equation and a factor of 1.1 to 2.0 speedup for the Euler equations, using the same processors and work distribution, and with the best possible configurations. These results show the potential effectiveness of the swept rule for different equations and numerical schemes on massively parallel computing systems that incur substantial latency costs.